Bonapace Giuseppe, Gentile Francesco, Coppedé Nicola, Coluccio Maria Laura, Garo Virginia, Vismara Marco Flavio Michele, Candeloro Patrizio, Donato Giuseppe, Malara Natalia
Laboratory of Genetics and Metabolic Diseases, Department of Pediatrics University Magna Graecia, 98100 Catanzaro, Italy.
BioNEM Laboratory, Department of Experimental and Clinical Medicine, University "Magna Graecia", 98100 Catanzaro, Italy.
Nanomaterials (Basel). 2021 Sep 18;11(9):2432. doi: 10.3390/nano11092432.
The altered glucose metabolism characterising cancer cells determines an increased amount of methylglyoxal in their secretome. Previous studies have demonstrated that the methylglyoxal, in turn, modifies the protonation state (PS) of soluble proteins contained in the secretomes of cultivated circulating tumour cells (CTCs). In this study, we describe a method to assess the content of methylglyoxal adducts (MAs) in the secretome by near-infrared (NIR) portable handheld spectroscopy and the extreme learning machine (ELM) algorithm. By measuring the vibration absorption functional groups containing hydrogen, such as C-H, O-H and N-H, NIR generates specific spectra. These spectra reflect alterations of the energy frequency of a sample bringing information about its MAs concentration levels. The algorithm deciphers the information encoded in the spectra and yields a quantitative estimate of the concentration of MAs in the sample. This procedure was used for the comparative analysis of different biological fluids extracted from patients suspected of having cancer (secretome, plasma, serum, interstitial fluid and whole blood) measured directly on the solute left on a surface upon a sample-drop cast and evaporation, without any sample pretreatment. Qualitative and quantitative regression models were built and tested to characterise the different levels of MAs by ELM. The final model we selected was able to automatically segregate tumour from non-tumour patients. The method is simple, rapid and repeatable; moreover, it can be integrated in portable electronic devices for point-of-care and remote testing of patients.
癌细胞所特有的葡萄糖代谢改变决定了其分泌组中甲基乙二醛含量的增加。先前的研究表明,甲基乙二醛反过来会改变培养的循环肿瘤细胞(CTC)分泌组中可溶性蛋白质的质子化状态(PS)。在本研究中,我们描述了一种通过近红外(NIR)便携式手持光谱和极限学习机(ELM)算法来评估分泌组中甲基乙二醛加合物(MA)含量的方法。通过测量含氢的振动吸收官能团,如C-H、O-H和N-H,近红外会产生特定的光谱。这些光谱反映了样品能量频率的变化,从而带来有关其MA浓度水平的信息。该算法对光谱中编码的信息进行解码,并对样品中MA的浓度进行定量估计。此程序用于对从疑似患有癌症的患者中提取的不同生物流体(分泌组、血浆、血清、间质液和全血)进行比较分析,这些流体是在样品滴铸并蒸发后留在表面的溶质上直接测量的,无需任何样品预处理。通过极限学习机构建并测试了定性和定量回归模型,以表征不同水平的MA。我们选择的最终模型能够自动区分肿瘤患者和非肿瘤患者。该方法简单、快速且可重复;此外,它可以集成到便携式电子设备中,用于患者的即时护理和远程检测。